University Of Wisconsin-Madison

NSF Awards · FY 2025 · 2025-10

Non-native species invasions are causing worldwide ecosystem degradation and economic loss, with average global economic costs exceeding 27 billion dollars per year over the past five decades. More urgently, both the number of non-native species and their impacts are projected to increase over the coming decades. For example, approximately an additional 1,500 non-native species are likely to establish in North America by 2050. Furthermore, the economic costs of biological invasions are predicted to increase threefold per decade. Government agencies, conservation organizations, and private citizens have spent significant resources to mitigate the impacts of species invasions, but the outcomes are far from satisfactory. One main reason is that we still do not have a holistic and predictive understanding of species invasion across scales. This project will compile an open-access, cross-scale database of species invasion centered around the datasets collected by the National Ecological Observatory Network (NEON). This database will be analyzed using advanced statistical methods to test theory on the relative roles propagule pressure, abiotic variables, and biotic variables on invasions for multiple taxonomic groups (plants, birds, and beetles) across spatial scales. Model results will be disseminated by building an online interactive application that can dynamically present and forecast risks of invaders at all NEON sites. This application will be updated automatically with new data to provide real-time management recommendations. One postdoctoral researcher and two undergraduate students will be trained in macrosystem biology, statistical, and data science skills during the project. The goal of this project is to test the relative importance of propagule pressure, abiotic variables (e.g., climate, land-use history), and biotic variables (e.g., species interactions) in driving species invasions across spatial scales in the context of community assembly. To achieve this goal, this project will improve the ability of phylogenetic generalized linear mixed model (PGLMM) to work with large datasets and then apply it to the integrated database of species invasions based on NEON to investigate patterns and mechanisms of biological invasions of different taxonomic groups across spatial scales. This project will address the following questions: 1) Do functional traits of non-native species interact with abiotic variables to determine their distributions? 2) Do biotic interactions between non-native and native species in the recipient communities affect the distribution of non-native species? 3) What is the relative importance of propagule pressure, abiotic variables, and biotic variables in determining the distribution of non-native species from local to continental scales? This project is jointly funded by the Division of Environmental Biology/Macrosystem Biology and NEON Enabled Science program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Some undergraduate students approach science learning as memorizing facts and formulas so they can pass examinations and courses. However, research shows that students' learning is deeper and more meaningful when science is approached as making sense of the world using broader concepts and practices of science. As such, improving student learning can involve changing students' understandings of what science learning entails. This project will seek to identify how different aspects of college science courses influence students' views of knowing and learning in science. Doing so is expected to provide college science instructors with ideas for how to change their courses to shift students toward viewing science as a useful way to make sense of the world, potentially leading to enhanced college science learning. This project seeks to build a mechanistic model for how students' epistemological understandings develop over the course of a semester. Specifically, the research team will unpack the epistemological landscape of three elements of undergraduate science courses – the instruction, the curriculum, and the assessments - to study how these elements affect students' views of knowledge and learning in science. The investigations will address the research questions: 1) Where and how are messages about valued knowledge products and processes embedded in undergraduate science courses? and 2) How do students experience and interact with these epistemological messages embedded in course systems? Across the three years of the project, these questions will be explored in the context of two college science courses: a large-enrollment general chemistry course at the University of Wisconsin at Madison and a small-enrollment introductory, conceptual physics course offered at a minimum-security state correctional institution. Very different course contexts were selected to examine whether the approach to modeling epistemological messaging is sufficiently robust and nuanced to be used in a range of settings. Three different types of data will be collected that each provide access to a part of the course system. First, all student-facing course materials will be collected including both formal artifacts such as syllabi and assessments as well as informal artifacts such as instructor communications. Second, multiple class sessions and small-group interactions across the semester will be recorded. Third, regular semi-structured interviews will be conducted with students to tap their in-the-moment reasoning about epistemologies embedded in the first two data sources. Both thematic and case-study analysis of epistemological messages will be conducted to identify consequential sources of messaging in undergraduate science courses as well as to model how students negotiate and respond to those messages across the semester. This project has the potential to advance theoretical understandings of the source and character of signals sent about valued knowledge products and processes in science and how those signals may combine to affect students' epistemologies. This work is also expected to provide insight into which components of course systems can be reformed to have maximal impact on student epistemological outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Plant phenology – the timing of plant life-cycle events, such as leaf growth, flowering, and fruiting – plays a fundamental role in shaping terrestrial ecosystems. The timing of plant phenology not only affects the fitness of individual plants, it also impacts the fitness and behaviors of organisms dependent on plants, which in terrestrial ecosystems includes nearly all animals, either directly or indirectly. Thus, changes in plant phenology can trigger dramatic, and sometimes devastating, consequences for ecosystems and human economic interests and health. Plant phenological data are therefore indispensable for understanding ecosystem function, detecting ecosystem changes, and predicting the impacts of ongoing climate and land use changes. Given the importance of plant phenology, continuing local, regional and national data collection efforts have generated large volumes of phenological data. However, these data are surprisingly heterogeneous, difficult to integrate, and thus remain largely inaccessible for broader research. At the same time, community science and specimen digitization infrastructure have produced massive, rapidly expanding collections of herbarium specimens and in situ plant photographs, which contain a wealth of virtually untapped historical and contemporary phenological information. This project will use machine learning approaches to extract phenological data from plant photographs and digitized specimens. These data will then be integrated with phenological monitoring resources to create an open access, global plant phenology database – Phenobase. During this project, one postdoctoral researcher and several graduate and undergraduate students will be trained in programming and data science skills. The goal of this project is to support community needs for generating and delivering high-precision, harmonized and semantically integrated plant phenological data at unprecedented taxonomic, geographic, and temporal scales, along with new tools to help scientists and the public engage with these data. To achieve this goal, this project will develop a global, standardized knowledge base by integrating different phenology observation networks around the world; expand this knowledge base by using computer vision (CV) techniques to generate new, high-quality phenological data from the rapidly growing collection of community-submitted plant photographs on iNaturalist and Budburst; add critical historical data by using similar CV techniques on herbarium specimens available through iDigBio and GBIF; develop tools for data query, access, and visualization delivered via the Web and as software packages; and foster compelling, community-driven use cases showcasing the use of Phenobase for new research and for public good. These approaches will not only meet current growth in imaging, but scale to meet continuing, exponential growth into the future. By weaving together phenologically relevant outputs from monitoring projects from around the globe, including the efforts of millions of community scientists, Phenobase will support and empower phenological research that is currently impossible. Results derived from this project can be found at http://plantphenology.org/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

A key challenge in STEM education research involves analyzing large amounts of learning process data from classroom interactions, collaborative problem solving in lab settings, and interactions with AI pedagogical agents and other learning technologies. To make informed decisions about curricula, teaching, and personalized instruction, educators working in technologically rich environments need learning analytic models that leverage teachers' qualitative insights as well as the large amounts of data that educational settings now generate, including log files from online learning environments and transcripts of conversations with AI agents. Quantitative ethnography (QE) provides a theoretical framework and set of tools to make sense of the natural language interactions common in traditional STEM learning as well as those occurring in rapidly evolving AI-based platforms. The goal of this Quantitative Ethnography Institute is to increase the capacity of STEM education researchers to use quantitative ethnography to address fundamental questions in STEM education research. QE is a set of statistical, computational, and AI-powered techniques that integrate qualitative and quantitative approaches to understand learning. This QE Institute will recruit three cohorts of 30 participants, the majority of whom will be early-career researchers, to engage in a year-long experience designed to support participants in using QE methods independently and train others to do so. Over the three years of this project, the QE Institute will provide intensive training, mentoring, and support to researchers. Participation will include 1) support in developing pilot QE analyses which will 2) be used in a weeklong intensive training in QE theory and methods followed by 3) one-to-one individual research consultations for the rest of the year. After completion of the Institute, participants will be able to conduct QE studies, lead interdisciplinary teams engaged in QE research, mentor and collaborate with junior researchers and colleagues, and lead QE workshops and trainings at their home institutions or at conferences they attend. This project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research. The project is also supported through a collaborative NSF activity with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to tackle the growing challenge of helping older adults and people with disabilities live safely and independently in their own homes. Many homes are not set up for their needs, and while home modifications can help, determining what changes are needed usually requires expensive and complex home assessments. This project will address this issue by using new technologies to make home assessments that are more efficient, accurate, and affordable. This research will explore issues in combining 3D sensing, augmented reality, and machine learning so that someone can automatically assess their living space as they move around in it, for example, determining if there is enough space around a coffee table in the living room for someone who uses a walker move safely. To be successful, the project must create methods for sensing the shape of the space and the objects in it, determining which objects and distances must be assessed, and displaying the assessments of them in a way that is accurate while being fast enough to be interactive for the user as they move around the space. The results of this work will enable more people to get the help they need to make their homes suitable for aging in place. This project aims to utilize emerging technologies in machine learning and augmented and mixed reality to design a new method for home assessments that is more efficient, accurate, and accessible. While traditional home assessments require highly trained individuals to perform tedious tasks in person, this research will enable adults who are aging with disabilities and their family caregivers to receive a service that is currently inaccessible to them. To automate the assessment, real-time object detection will be combined with spatial tracking and scene understanding through augmented reality devices to create a labeled model of the space. This project will require creating novel methods for achieving high-fidelity 3D spatial accuracy in home-sized settings while preserving interactivity on the augmented reality device. The project will also require developing methods to automatically detect and segment objects and features of the space that must be assessed. From a human factors standpoint, the project will investigate the comparative advantages of mobile augmented reality and head-mounted display systems for augmented assessment tasks. The technologies will be developed under the guidance of housing and healthcare professionals alongside general users. The developed systems will be evaluated in the field to determine if they are appropriate, acceptable, and feasible for adults who are aging with disabilities and their family caregivers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Catalyst materials that speed up chemical reactions play a critical role in the production of energy and chemicals. The catalyst can change during this process, as metal atoms rearrange on the nanoscale, forming new structures with distinct properties and performance. Manipulating such changes could lead to improved materials for industrial reactions, but research progress has been limited by a lack of general principles to understand and control catalyst dynamics. To address this challenge, researchers will integrate advanced computer modeling, accelerated by artificial intelligence and machine learning, with experimental tools to study how catalyst structures evolve during reactions. This workflow enables efficient screening of a wide range of materials to accelerate the discovery and design of more effective catalysts by controlling their dynamics. Specifically, the project will study ammonia fertilizer production, which supports global food supply but is highly energy-intensive (~2% of annual global energy consumption goes to this process), to guide the design of new energy-efficient catalysts. The project will also study how ammonia can be used as an energy carrier through cracking to hydrogen over earth-abundant catalysts. Interdisciplinary training of graduate students in state-of-the-art computer modeling and experimental methods, combined with educational outreach efforts to K-12 students, will prepare students to become leaders in catalytic materials design. This project will construct a unified, predictive model of the dynamic restructuring of metal nanoparticles on metal-oxide supports by elucidating the effects of materials properties and reaction environments on dynamic catalyst performance. In turn, these principles will enable the design of more active, stable, and ‘self-healing’ materials for industrially relevant ammonia synthesis and cracking reactions by tuning material properties to stabilize the most active nanostructures under reaction conditions, and enabling regeneration treatments that reverse the deleterious effects of catalyst sintering. The research team will develop a closed-loop workflow to integrate ab initio molecular modeling and artificial intelligence/machine learning (AI-ML) tools to efficiently screen materials composition space, combined with experimental synthesis of shape-controlled metal nanoparticles on metal-oxide supports, in situ characterization of dynamic behavior using high-resolution microscopy and spectroscopy, and high-throughput reactivity evaluation using steady-state and transient methods. Insights from this project will be used to develop more energy-efficient and stable non-precious metal catalysts for catalytic ammonia synthesis and ammonia cracking to hydrogen. The general principles developed here will have broad relevance to industrially important catalytic reactions involving catalyst restructuring. Databases and AI/ML workflows will be made publicly available to enable use of research products by the catalyst materials community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Designing Materials to Revolutionize and Engineer our Future (DMREF) joint NSF-Department of Science and Technology of India (NSF-DST) project aims to establish a transformative framework for the development of structural alloys that simultaneously achieve high strength at high temperatures and enhanced ductility at room temperature. The research focuses on a relatively new class of metallic materials known as refractory multi-principal element alloys (RMPEAs), which are recognized for their high-temperature strength but typically suffer from limited plasticity under ambient conditions. The team will develop the new alloy design paradigm through a concept called “metastability engineering,” which activates novel nano-scale deformation mechanisms by controlling dislocation dynamics and phase stability. The research integrates combinatorial synthesis, advanced in-situ experiments, atomistic and mesoscale simulations, and machine learning (ML)-guided discovery. The resulting framework will enable accelerated design of high-performance RMPEAs across broad temperature ranges. In parallel, the project will contribute to training a new generation of materials scientists in experimental, computational, and data-driven methods, while supporting outreach and international collaboration through partnerships with five US universities and Indian Institute of Technology Bombay. This project aims to establish a transformative framework for metastability engineering in refractory-type multi-principal element alloys (RMPEAs) that combines high-temperature strength with improved room-temperature ductility and strain hardenability. This project will address two key technical thrusts: (1) understanding dislocation dynamics for solid-solution strengthening at both room and high temperatures, and (2) enabling nano-scale transformation-induced plasticity (nano-TRIP) and twin-induced plasticity (nano-TWIP) mechanisms for enhancing ductility at room temperature. To navigate the vast composition and processing space, the team will integrate combinatorial synthesis, high-throughput and autonomous mechanical testing, and advanced machine learning techniques to accelerate the discovery of high performance RMPEAs. In the first thrust, the project will quantify the contributions of dislocations to high-temperature strength through autonomous nanoindentation creep testing, in situ neutron diffraction, and atomistic simulations. Advanced microscopy techniques will be used to reveal how local chemical ordering and lattice distortion affect dislocation motion. In the second thrust, the team will identify composition-processing pathways that promote metastable deformation modes using thermodynamic modeling, combinatorial deposition, and transformer-based machine learning models. These models will predict TWIP/TRIP propensity and guide multi-objective optimization across large alloy design spaces. Down-selected alloy systems will be validated through multiscale mechanical testing and simulations that span atomic to bulk scales. Collectively, the project will deliver a mechanistic foundation and data-driven design tools for metastability engineering in RMPEAs, aligning with the DMREF project’s mission to accelerate materials innovation through the integration of theory, experimentation, and data science with closed-loop design cycles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Changes in climate are significantly altering the freshwater habitats of inland lakes and rivers. Despite concern over the impacts of a changing climate on aquatic ecosystems, some lakes and rivers are holding up surprisingly well. The aquatic ecosystems of northern Mongolia represent one such “bright spot.” While air temperatures in the region have warmed almost three times faster than the global average, coldwater fish populations in northern Mongolia appear to be surprisingly robust. This project builds on nearly 20 years of ecological research, monitoring, and sample collections to understand the factors underlying the resilience of Mongolia’s lakes, rivers, and fish populations. Each summer, four undergraduate and four graduate students travel with American and Mongolian scientists to Lake Hovsgol and the Eg River in Mongolia to engage in research for six weeks. Here, they conduct field ecology research projects in two-person grad-undergrad teams. Student participants hone their research project proposals and outreach plans through a pre-trip distributed seminar and present results after the expedition at an online virtual symposium. Student research teams investigate three broad hypotheses about the factors contributing to resilience of aquatic ecosystems. First, via “behavioral thermoregulation,” fish may be able to select microhabitats where temperatures remain more favorable to growth and survival. Second, intra-specific variation in characteristics such as temperature tolerance may allow populations to persist in warming waters. Third, changing ecological interactions, including predator-prey and host-parasite relationships, may either offset or exacerbate the direct effects of warming. Experiments and observational studies test these hypotheses using cutting-edge tools such as DNA metabarcoding to investigate diets and microbiomes of endangered fishes, electronic tags to track fish movements, and data logging sensors to understand variation in the abiotic environment. The Intellectual Merit of this project is centered on improving our understanding of the effects of a changing climate on natural ecosystems. The Broader Impacts include improved management approaches for U.S. fisheries impacted by changes in climate and the training of 24 U.S. students in aquatic ecology research methods in an international setting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Non-technical description: Twisted oxide heterostructures are artificial materials made by stacking two or more complex oxide thin layers on top of each other with a twist angle — meaning one layer is rotated relative to the other. This twist creates a moiré pattern at the interface — a repeating interference pattern that changes the local atomic arrangement and the electronic environment. This can dramatically alter the material’s properties and create novel functionalities useful for applications. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to design, create, and understand novel electronic, magnetic, and structural phases emerging in free-standing oxide membranes assembled into twisted heterostructures. The research combines advanced characterization techniques with theoretical modeling and data analytics to accelerate the discovery and development of new materials with engineered properties. The project leverages an iterative feedback loop between theory, synthesis, and characterization and involves the U.S.-Israel collaboration supported by Binational Science Foundation (BSF). Educational and outreach activities within this project are targeted at advancing workforce development through interdisciplinary training of graduate students and postdoctoral researchers in integrated experimental and theoretical approaches to materials research. Technical description: This DMREF project aims to explore fundamental phenomena emerging in oxide moiré heterostructures, including structured two-dimensional polarization-vortex crystals, topological spin textures at twisted oxide interfaces, oxide flat-band systems at large twist angles, coupled quantum dot arrays, and dynamically strained interfacial electron and hole gases. The project introduces moiré periodicity and modulated intra-moiré-cell atomic registry as new design parameters in thin-film oxides. The strong interlayer coupling in oxide systems generates a strong periodic potential, enabling robust quantum states and new physical phenomena through the interplay between intrinsic oxide properties and moiré-engineered periodicity. The research combines state-of-the-art theoretical modeling approaches, advanced synthesis techniques for creating oxide membranes, and unique characterization methods, particularly Quantum Twisting Microscopy, which enables momentum-resolved spectroscopy with nanoscale spatial resolution. The education/outreach component of this project includes DMREF Workshops providing training experience for students and postdoctoral researchers, collaboration with secondary school teachers from Puerto Rico and Wisconsin to develop teaching modules incorporating DMREF principles, and integration of the undergraduate research with a First Experiences in Quantum program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Non-technical Description: The mass production of integrated circuits (commonly known as ‘microchips’ or simply ‘chips’) is a key driver for modern computational advances. Chip manufacturing requires a process called photolithography to template the intricate patterns of electronic components. This process uses patterns of light to selectively pattern a material known as a photoresist. New extreme ultraviolet (EUV) based lithography methods are poised to enable more powerful chips than ever before by packing higher volumes of smaller electronic components onto a single chip, making new photoresists essential to reaching the desired small features sizes. This Designing Materials to Revolutionize Our Future (DMREF) project combines chemistry, processing, and computation to design new photoresists to enable high-volume EUV lithography for chip manufacturing. This will be achieved by understanding how the local molecular structure of polymer-based photoresists defines the patterning at nanoscale dimensions, and how this translates to manufacturing outcomes. This interdisciplinary effort will bring together scientists and engineers from academia and the Air Force Research Lab with expertise in synthesizing materials, characterizing their physical properties, modeling their behavior with simulations, and predicting new materials with improved properties using AI. The new materials and patterning methodologies developed in this project will broadly benefit the US by enabling advanced manufacturing of next-generation computer chips with applications ranging from personal electronics and health care monitoring to supercomputers and generative AI. This research will further be combined with K-12 outreach and student training to prepare the next generation STEM workforce. Technical Description: This project will integrate combined expertise in polymer chemistry, physics, computation, and advanced manufacturing into a closed loop process to enable the design and implementation of crosslinkable polymeric photoresists for EUV lithography. Theory, molecular simulation, and data science will be combined with polymer chemistry and advanced metrology to understand how the sequence-specific molecular structure of copolymers translates to local patterning heterogeneity. Additionally, this effort will be combined with data science-enabled proxy measurements to rapidly and efficiently traverse an enormous chemical space for materials discovery. The ultimate goal of this work is to develop candidate chemistries that produce patterns with appropriate dimensions and fidelity under industrially-relevant EUV exposures. More broadly, workflows to aid the discovery-to-translation timeline of EUV lithography resists will be developed. Additionally, this research will be integrated with education and workforce development efforts to train students who can effectively communicate across the materials development continuum and contribute to the semiconductor industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Wisconsin Opioid Prevention and Treatment Research Network (WI OPTRN) Abstract The opioid overdose crisis remains a major public health emergency, causing over 81,000 deaths in 2023. Emergency departments (EDs) are key settings for intervention, as they frequently treat individuals with opioid overdoses or those at high risk for overdose. Despite this, evidence-based practices such as ED buprenorphine initiation and linkage to ongoing care remain underutilized, representing a significant gap in the continuum of care for individuals with opioid use disorder (OUD). A multidisciplinary team, including addiction specialists, emergency medicine physicians, peer support specialists, human factors engineers, data scientists, and implementation scientists, seeks to address this gap by improving buprenorphine adoption in EDs. The team will use advanced data-driven methods embedded in the electronic health record (EHR), combined with peer-supported care continuity. Surveys of ED clinicians at two University of Wisconsin EDs revealed that clinicians reported that clinical decision support would be a key facilitator for initiating buprenorphine. In response, a clinical pathway for buprenorphine initiation was implemented in 2023, but a follow-up evaluation showed that only 23% patients whose ED visit had a diagnosis of opioid use disorder or opioid overdose received buprenorphine, indicating missed opportunities. To address this, the team proposes an approach integrating artificial intelligence (AI), EHR-embedded clinical decision support (CDS), and peer navigation to improve buprenorphine initiation and linkage to care. The team brings expertise in addiction medicine, implementation science, human-centered design, and clinical informatics, along with input from a Community Advisory Board (CAB) of people with lived experience. The project has three aims. Aim 1 will validate the Opioid AI Screener, a tool previously validated in inpatient settings, to identify patients at high-risk for OUD in the ED with greater than 85% sensitivity and specificity (years 1-4). Aim 2 will involve co-designing and implementing an EHR-embedded CDS to increase buprenorphine initiation in the ED. This system will prompt clinicians when high-risk patients are identified, and offer step-by-step guidance for buprenorphine initiation, management, and automated referral to an outpatient substance use disorder clinic (Years 1-4). Aim 3 will focus on strengthening linkage to community-based OUD care through peer support specialist (PSS) navigation. PSS will conduct outreach, help patients find follow-up care, and address barriers to treatment engagement (Years 1-4). The overarching goal of this project is to create an implementation guide to support wider adoption of buprenorphine initiation in EDs, improving patient outcomes and contributing to broader overdose prevention efforts.

NIH Research Projects · FY 2025 · 2025-09

Abstract Over the past five decades, autism prevalence in the United States has risen from fewer than 1 in 2,000 children to approximately 1 in 31. Over the same period, early-life exposures—such as parental age, maternal diabetes, and prenatal complications—have changed dramatically. These parallel trends raise urgent questions: Are these early-life factors contributing to the increase in autism diagnoses? And how might these factors shape outcomes that are meaningful to autistic individuals and families? This project addresses these questions by applying modern causal inference methods to large, population-based datasets. Unlike traditional regression models, which estimate associations, causal models allow researchers to simulate what would happen if a single exposure were changed while holding all else constant. This enables valid estimation of both how much a risk factor contributes to autism diagnosis and related outcomes—such as communication or emotional regulation—and how much of the increase in autism prevalence can be attributed to historical shifts in these factors. By combining these models with flexible, data-driven approaches that capture the heterogeneity in autism, the project may also identify distinct pathways by which early-life factors may lead to different autism phenotypes. The study has four specific aims. First, researchers will prepare two complementary datasets for causal analysis: (1) the Study to Explore Early Development, a multisite case-control study led by the CDC that includes extensive behavioral, developmental, perinatal, and biological data; and (2) electronic health records from a large Midwestern health system linked to birth certificates. Second, causal models will be used to estimate the effect of early-life exposures on autism occurrence. Third, exposures will be jointly modeled with autism phenotypes to uncover causal pathways that may be obscured by traditional analytic approaches. Fourth, these models will be applied to longitudinal data to estimate the portion of the increase in autism prevalence attributable to historical shifts in early-life exposures. Findings will clarify which early-life factors have the strongest causal effects, which developmental outcomes they influence, and the extent to which they may have contributed to rising autism prevalence. Results will inform strategies for intervention and early identification and provide a foundation for prioritizing biological mechanisms and modifiable exposures. Code and models will be publicly released in a modular, template-based format to enable replication across datasets such as CHARGE, SFARI, All of Us, and TriNetX. The research team brings expertise in autism epidemiology, causal inference, clinical informatics, and participatory methods and includes individuals with lived experience of autism, ensuring that the work is rigorous, actionable, and grounded in the perspectives of the autism community.

NIH Research Projects · FY 2025 · 2025-09

The Wisconsin State Laboratory of Hygiene (WSLH) is Wisconsin’s premier Public and Environmental Health Laboratory and is part of the world-renowned University of Wisconsin – Madison School of Medicine and Public Health, with a mission to improve and protect the human condition by providing accurate and precise testing, service, research and education. The Radiochemistry Department is housed within the Environmental Health Division (EHD) of the WSLH. The Radiochemistry Department has been a member of the Food Emergency Response Network (FERN) for 18 years and interacts with members from state agencies such as the Wisconsin Department of Health Services and federal agencies: FDA, FEMA, and EPA. The funding provided by the FERN agreements has been critical in the Radiochemistry Department’s success. It has enabled the department to maintain readiness to analyze food samples if called upon during a radiological food emergency. The Radiochemistry Department is applying for all three tracks (C1, C2, C3) in the Radiochemistry Discipline listed in NOFO: RFA-FD-25-007. The department has 4 chemical fume hoods for sample processing and separation, 6 gamma spectrometers (HPGe detectors), 2 alpha spectrometers (8 channels each), 4 gas proportional counters (4 detectors each), an automated gas proportional counter (50 sample capacity) and a liquid scintillation counter. The Trace Elements Clean Laboratory (housed within EHD at WSLH) has ICP-MS instrumentation that could also be utilized for analysis in a radiological food emergency. The department has trained staff experienced in analyzing alpha emitters (Am, Pu, Ur, gross alpha), beta emitters (Sr-90, H-3, gross beta), and gamma emitters for a variety of matrices (water, air, milk, vegetation, soil, fish, and an assortment of foods (successfully completed all FERN food challenge samples). In addition, the laboratory has worked on a variety of FDA sponsored projects, recent projects include: “LFFM Multi-Lab Validation Study on Screening for Gross Alpha and Beta in Food using GPC and LSC” and “Radiostrontium Matrix Extension Study”. The unit is audited and accredited by the EPA and was recently audited by FEMA with no deficiencies cited. The unit has validated SOPs in place to analyze a variety of radionuclides with different matrices and adheres to strict quality control parameters that includes participation in a variety of proficiency testing programs to show competency. We are centrally located within the United States, in a critical part of the nation’s food network. We also have numerous power plants located in or near the state’s border that we provide surveillance for (over 900 surveillance samples per year). Our laboratory has significant experience in emergency response events and exercises. The Radiochemistry Department has the instrumentation, validated methods, experienced staff and the knowledge in place, as well as the capacity and capabilities, to analyze samples in the event of a radiological food emergency and to exceed all aims specified in the funding opportunity. The funding associated with this grant is critical in maintaining these capacities and capabilities.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains a major global public health problem in children. In 2022 there were approximately 1.3 million new cases of TB in children <15 years of age, resulting in 214,000 deaths. TB treatment outcomes for pulmonary TB in children are good, but the current 4-6 month treatment durations are long, burdensome and costly. Shortened TB treatment regimens would be highly beneficial to children with TB, their caregivers, and TB programs, and are appropriate to evaluate given the very low burden of organisms (paucibacillary) in children compared to adults. Emerging data support that durations shorter than currently recommended are possible for children. A highly attractive candidate regimen for shorter treatment would use optimized doses of rifampicin (odRIF), a uniquely important and widely available existing TB drug. This approach has shown promising results in adult trials, and we completed a pediatric trial demonstrating the pharmacokinetics (PK) and safety of odRIF over 2 weeks. Definitive TB treatment shortening trials are large and costly, and are a high risk to funders and participants, especially due to challenges in selecting optimal treatment durations, particularly for children. The phase IIc duration randomization trial design models the duration-response curve of an intervention and addresses many of these challenges. This design is more efficient, less expensive, and de-risks future phase III trials. This innovative trial design is being used in adults with TB, but to date has not been applied to children with TB. Evaluating novel shorter TB treatment regimens requires a holistic approach that includes patient- centered outcomes in addition to assessment of efficacy, safety and tolerability. Previous pediatric trials have not included assessments of lung health and quality of life, resulting in limited data on these important outcomes and no data comparing these outcomes to children receiving different TB treatment regimens or durations. Data on these important patient-centered outcomes and on child and caregiver priorities and preferences for shortened TB treatment are critical yet neglected to date in trials. We will undertake REDUCE-TB, an innovative multi-center multi-arm open-label phase IIc trial using a duration randomization design with a treatment regimen of optimized dose RIF (od-RIF) with standard doses of isoniazid, pyrazinamide and ethambutol, in children 3 months to <10 years of age with drug- susceptible TB. Prior to the main trial, a Lead-in study will confirm the PK, safety, and tolerability of odRIF. Key patient-centered outcomes will be evaluated. This study will de-risk future definitive phase III treatment shortening trials in children by identifying the optimal duration of shortened treatment, leading directly to better, shorter TB treatment for children. This trial will also develop the methodological framework for use of duration randomized treatment trials in children with TB more broadly and advance more holistic trial approaches that include and rigorously evaluate key patient-centered outcomes.

NIH Research Projects · FY 2025 · 2025-09

Abstract Globally, over one million people rely on cochlear implants (CIs) to hear and listen. However, inter-individual variance is high and ranges from the effective absence of functional communication to almost typical hearing levels. Most CI users do not attain speech comprehension scores close to people with typical hearing for often unclear reasons, as there is a significant lack of predictors and objective markers for CI therapy success. To aid the NIDCD’s mission to help prevent, detect, diagnose and treat hearing disabilities, it is crucial that we understand more about when and why CIs do and do not work. While most current research focuses on device technology and the electrode-nerve-interface, it is widely appreciated that an integral part to CI rehabilitation is brain adaptability. In fact, clinical results showing increased speech understanding over time dependent on onset and duration of deafness strongly hint at a major role of experience-dependent brain plasticity and perceptual learning for CI outcomes. Despite this significance, the exact neural changes induced by chronic CI use remain largely unknown even though they can be recorded in animals, a gap that this proposal is aimed at narrowing. The long-term goal of this research is to advance and develop CI stimulation strategies in ways that optimally harness, utilize, and support brain function and plasticity. There is increasing evidence that sensory cortical ensembles form small functional networks crucial for experience- dependent plasticity and perceptual learning. Cortical rhythmic synchronization associated with such sensory circuits and local small networks is implicated with phoneme discrimination and speech understanding in noise in human CI users. The aims of this research proposal are constructed to identify those cortical population activity mechanisms that contribute to CI success. The experiments focus on how small networks and functional oscillations commonly activated during perceptual learning are disrupted during CI- guided learning. All aims will be achieved with daily brain activity recordings in acutely deafened mice learning to use CIs during behavior. We will first chronically record high-density intracortical activity as mice learn to interpret CI stimulation in a reward-based operant conditioning task over several weeks, hypothesizing that small network formation is disrupted due to increased correlated variability elicited by hypersynchronizing CI stimulation. We will further test cortical oscillatory activity measured by EEG as a predictor and objective marker of CI behavioral outcome in mice. All measures taken will be correlated with behavioral performance and learning speed to find candidate mechanisms of interest for future R01 studies tracking and manipulating detailed networks activated during CI-guided behavior to increase behavioral performance and learning speed. The completion of our aims is thus a critical first step in unlocking the full potential of CI technology by taking into account the brain’s heavy lifting during CI rehabilitation – an urgent endeavor given that an estimated 75 million Americans and 700 million people worldwide will suffer from moderate to complete hearing loss by 2050.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT People with Down syndrome (DS) have increased susceptibility to immunological pathologies, certain cancers, congenital heart defects, and cognitive decline, all of which involve immune dysregulation. Elevated inflammatory cytokines associated with DS may impact disease treatments, such as immunotherapy for leukemia and organ transplantation. Current mouse models do not fully replicate the unique immunobiology of human trisomy 21, highlighting the need for more robust animal models to study DS-specific immune responses to therapies. Humanized mouse models, created by introducing human hematopoietic cells into immune-deficient mice, offer unique opportunities to evaluate pharmacological and cellular therapies in a DS context. No DS humanized mouse models of this class have been reported until now. We developed a DS humanized mouse model, the Thymocyte-Hu, using neonatal thymocytes. Preliminary data show long-term human T cell engraftment and reduced graft-vs-host disease susceptibility. This R24 project aims to fully characterize the phenotype and function of engrafted T cells in Thymocyte-Hu mice, which will provide a better understanding of DS T cell maturation and antigen-specific responses in the context of clinical translation of hypoimmune induced pluripotent stem cell (iPSC) therapies for people with DS. Additionally, we will leverage another new DS NeoThy model, combining umbilical cord blood hematopoietic cells and neonatal thymus fragments to study DS T cell development and function within a full immune repertoire. In our Specific Aims, we will establish multiple biological material and data resources for the DS research community, by: 1) creating mosaic DS hypoimmune gene-edited iPSC lines. We will differentiate these cells into cardiovascular cell therapies and assess in vitro DS immune responses; 2) characterizing Thymocyte-Hu model T cell phenotype and function using bioluminescence imaging, single cell RNA sequencing, Luminex cytokine assays, and flow cytometry; 3) assessing DS NeoThy humanized mouse T cell dynamics and immune responses to hypoimmune iPSC cardiovascular cell therapies using single cell RNA sequencing, epigenomics, bioluminescence imaging, Luminex, flow cytometry, and histology; and 4) sharing biological and data resources by distributing humanizing tissues, gene-edited iPSC lines, and experimental data and by providing access to humanized mice through the University of Wisconsin Humanized Mouse Core. Successful completion of this project will provide novel DS humanized mouse models and data resources to the research community, fostering high-impact discoveries and potential new therapies for people with DS.

NSF Awards · FY 2025 · 2025-09

Collaborative Research: Research Initiation: Mixed Methods Study of Rural Engineering Students' Sense of Belonging at a Midwestern Research-Intensive University This project aims to serve national priorities by initiating research on the professional formation of engineers and supporting pathways into and through engineering for students with varied backgrounds, interests, and experiences. Meeting evolving workforce needs requires not only more engineers but professionals whose perspectives reflect a spectrum of experiences, including those shaped by rural communities. Research universities play a vital role in cultivating the next generation of engineering talent. However, fostering environments where students can thrive requires more than simply providing access—it also requires a sense of belonging. For many, feeling connected is essential to staying engaged and persisting in their studies. When that connection is lacking, the risk of leaving the field increases, limiting opportunity and reducing the breadth of ideas that drive innovation. Despite this, little research has examined how rural engineering students experience belonging at large universities—a gap this project will address. This National Science Foundation Research Initiation in Engineering Formation (RIEF) award to the University of Wisconsin–Madison and Clemson University will explore rural engineering students’ sense of belonging and identify opportunities to strengthen connectedness. Using surveys followed by in-depth interviews, the research will gather rich, first-hand accounts to contribute new knowledge about varied experiences in engineering education. These insights will inform efforts to improve retention and help ensure that talented students from all communities can advance into engineering careers. This study aligns with the goals of the NSF RIEF program by advancing innovative research on engineering formation while strengthening the capacity of early-career faculty to lead impactful educational studies. Ultimately, the findings will support a more resilient engineering workforce enriched by a broad range of perspectives. The goals of this research are to explore rural engineering students’ sense of belonging, identify opportunities to improve connectedness, and develop monitoring programs and targeted interventions for students at risk of leaving the engineering pipeline. The study will address three research questions: (1) What factors influence a student's self-identification as rural and how strongly do they identify with their rural background or experiences? (2) In what ways does this identification shape their college experience, environment, and sense of belonging? (3) How can institutions better support students in strengthening their sense of belonging earlier in their college journey? A mixed-methods approach will begin with a quantitative survey available to all students. All students will be included and participants will indicate whether they consider themselves to have rural backgrounds or experiences, and those who self-identify as rural will be included in the analysis. Allowing students to define rurality in their own terms will yield nuanced insights into how rural identity relates to their experiences. Survey findings will guide the selection of participants for follow-up semi-structured interviews to gather in-depth qualitative data. Analysis will draw on social identity theory, ecological systems theory, and belongingness theory to build a multidimensional understanding of these factors. Expected outcomes include evidence-based strategies and tools to help institutions create supportive environments, identify students at risk of disengagement, and implement targeted interventions. More broadly, this work will inform and support efforts to expand opportunities for aspiring engineers by deepening understanding of how identity influences pathways into and through engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Individuals with Down syndrome (DS) represent the largest population of genetically determined Alzheimer’s disease (AD) in the world. This genetic form of AD is driven by triplication of chromosome 21, which encodes the gene responsible for amyloid precursor protein production, leading to earlier beta-amyloid plaque (Aβ) deposition. Advances in health care specific to this population have resulted in individuals with DS living longer, healthier lives. With this significant increase in life expectancy, AD is now the leading cause of death for individuals with DS over the age of 35, accounting for 70% of deaths in this demographic. Despite a >90% incident rate of AD dementia by age 60 for individuals with DS, this population has been overlooked for disease- modifying therapeutic trials. The research effort in AD therapeutics has rapidly progressed, with promising findings from recent “anti-amyloid” therapy clinical trials at delaying the onset of cognitive decline. For DS inclusion in these trials, many challenges exist pertaining to recruitment and design such as understanding the differences in biomarker progression between DS and neurotypical (NT) adults, their intellectual disability and their age at biomarker and symptom onset. Furthermore, there exists a 30-year span in the age at which AD biomarkers and dementia emerge in this population, which greatly hinders age-based trial recruitment. Minimally invasive and cost-effective methods for trial recruitment, such as blood-plasma measures, also have yet to be characterized in DS. To develop a better understanding of AD biomarker onset and progression, we have utilized positron emission tomography (PET) imaging in the DS population to evaluate longitudinal changes in amyloid, tau and neurodegeneration prior to dementia onset. This project will address this public health need by characterizing AD biomarker progression in the DS population at various stages of the disease, allowing for better insight into the initiation of AD treatment based on biomarker level and disease stage. To pinpoint suitable treatment windows at varying stages of DSAD, this project will model AD biomarker progression through the novel Biomarker Clock Framework, which creates a timeline of disease progression based on the onset of abnormal biomarker presence. This work will be carried out through three specific aims: 1) characterization of amyloid PET, plasma Aβ42/Aβ40 and p-tau217, 2) characterization of longitudinal tau PET within the revised AD staging framework, and 3) evaluation of cognitive decline on the AD timeline. Identifying the earliest changes in AD biomarkers will facilitate anti-amyloid clinical trial designs and the recruitment of individuals with DS into early intervention and secondary prevention trials. With the biomarker onset times developed in this proposal and with the direct comparisons to measures in NT cohorts, biomarker progression timelines will be developed to define clear therapeutic windows for AD treatment in DS, allowing for treatments specifically catered to these individuals.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT My career goal is to optimize rehabilitation, improve treatment outcomes, and mitigate the debilitating long-term consequences from lower extremity tendon injuries. In this study I propose to enhance the treatment for patellar tendon injuries. The patellar tendon is a key structure for force transmission, mobility, and locomotion. The best evidence-based treatment for tendon injuries involves tendon loading via exercise; however, establishing optimal dosing remains a challenge. Clinical trials have prescribed exercises assuming external loads serve as surrogates for tendon load, however, this assumption is not entirely accurate due to commonly observed compensations at adjacent joints in patients with knee injuries. Thus, methods to directly measure tendon load are needed. Shear Wave Tensiometry (SWT) is a clinically feasible, wearable technology that can directly measure tendon load in-vivo. Neuromuscular Electrical Stimulation (NMES) during exercise is a promising approach that may improve outcomes by augmenting tendon load, yet its direct effects on tendon load have not been quantified due to lack of innovative technology to study the effects. Current methods for assessing recovery from tendon injuries are also insufficient for accurately capturing effectiveness of treatments. Tendon pathology observed at the macrostructural level does not always relate with symptoms, and improvements in symptoms tend to precede changes to tendon macrostructure. Diffusion Tensor Imaging (DTI), a magnetic resonance imaging based method to measure tendon microstructure in-vivo, offers potential to better study tendon pathophysiology and treatment response. This project aims to capture tendon adaptations at the microstructural level using DTI in response to a rehabilitation program focused on tendon loading quantified via SWT and enhanced by NMES. Two patellar tendon injuries will be studied: patellar tendinopathy and bone-patellar tendon- bone autograft harvest for anterior cruciate ligament reconstruction. Aim 1 will establish DTI methodology to quantify patellar tendon microstructure and the relationship with knee function in patients with patellar tendon injuries. Aim 2 will quantify patellar tendon loads using SWT during key rehabilitation exercises with and without NMES in patients with patellar tendon injuries and in a healthy cohort. We will rank tendon loads during exercises and compare the rankings between patients with patellar tendon injuries and healthy controls. The ability of NMES to augment tendon load will also be studied. Lastly, Aim 3 will develop and determine the preliminary efficacy of a SWT-informed rehabilitation protocol and quantify the treatment effects using DTI in patients with patellar tendinopathy. In a 12-week clinical trial, we will assess improvements in patient symptoms and function, and if DTI can capture acute microstructural changes in response to treatment. Through the K99/R00 award, I will gain the qualifications and preliminary data necessary to independently lead randomized controlled trials. This research proposal is expected to broadly impact tendon injury rehabilitation, potentially improving treatment for multiple tendons and pathologies.

NIH Research Projects · FY 2025 · 2025-09

PROJECT ABSTRACT An estimated 7 million people live with Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) in the United States alone. While AD/ADRD is characterized by progressive losses in cognition and functioning, a rapidly developing body of evidence demonstrates that some individuals with advanced AD/ADRD exhibit a seemingly implausible transient recovery of function, evidenced by a return of communication and/or functional behaviors after these abilities were believed to be irretrievably lost. These events, referred to as Lucid Episodes (LE), represent a unique presentation of resilience in advanced disease. Until recently, research on LE in AD/ADRD had been sparse. Recent findings from a set of NIA- funded studies, however, demonstrate that LE occur throughout the late-stage disease course, including near end of life, and have marked effects on caregivers and their decision-making. Findings from these and other studies highlight the considerable measurement deficits and limitations that need to be addressed in order to advance systematic investigation on LE and their impact on care delivery and caregiving. Coordinated efforts and interdisciplinary collaborations are needed to address these measurement challenges. To address this need, we propose to establish the Lucidity in Alzheimer’s and Dementia (LEAD) Network which will assemble an integrated research infrastructure to facilitate interdisciplinary collaborations, bolster research capacity, and advance measurement science on LE in AD/ADRD. The LEAD Network will first identify research priorities and strategies to advance measurement of LE in AD/ADRD and their impact on care delivery and caregivers (R61 Phase, Aim 1). Then, the LEAD Network will establish an operational research infrastructure to support the development, testing, refinement, and dissemination of measures for LE in AD/ADRD, including a Common Data Elements (CDE) repository and secondary data resource library (R61 Phase, Aims 2, 3). Next, we will leverage this infrastructure to support pilot studies that address identified research priorities (R33 phase). Specifically, the LEAD Network will provide direct support to 6 pilot projects, including ongoing consultation, mentoring, and post-award guidance, fortifying awardees to transition toward full-scale funded projects (R33, Aim 1). Additionally, the LEAD Network will disseminate resources to support research on measurement of LE by a) holding regular training workshops, b) hosting webinars and giving conference presentations, c) providing consultation, and d) sponsoring a conference for dissemination of findings from measurement studies and interdisciplinary networking (R33, Aim 2). Collectively, these activities will create and solidify a sustainable infrastructure upon which future data and resources can be integrated and nurture interdisciplinary relationships and networks necessary to catalyze investigations to advance measurement of LE in AD/ADRD.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Degenerative cardiac remodeling from chronic overload is a significant contributor to cardiovascular disease. Signaling pathways initiated by pathologically elevated myocardial stretch are linked to multiple stretch-sensitive mechanisms, however, the signaling events that occur at the cell membrane to sense and transmit mechanical signals, at both physiological and pathological conditions, are still poorly understood. We identified neutral sphingomyelinase (nSMase), a membrane hydrolase enzyme involved in sphingolipid metabolism reactions, as a critical component of cardiac mechanosensing and mechano-chemical signal transduction. Our findings indicate that nSMase plays an important role in the regulation of normal physiological function of the heart upon acute changes in cardiac load, including autoregulatory chronotropic and inotropic responses. At the same time, chronic mechanical stress results in constitutively active nSMase increasing ceramide production and accumulation in damaged myocardium. It leads to deterioration of sarcolemmal membrane, elevated production of reactive oxygen species, blunted β-adrenergic response, and increased arrhythmogenesis, as also observed during chronic heart failure in patients and animal models of chronic cardiac overload. Moreover, mechanical and environmental stress is known to provoke certain genetically predisposed arrhythmogenic cardiomyopathy (ACM) to transition from quiescent to disease phenotype, yet no modulation methods or detection methods are available. These results position nSMase and associated signaling as key pathways for remodeling in heart failure and represent promising therapeutic options for mitigating the deleterious effects of cardiac pressure overload. However, there is limited research on nSMase in the heart. In this application, we will address this gap in knowledge and will determine the role of nSMase in mechanical regulation of cardiac function in both physiological conditions and during chronic cardiac overload. We hypothesize that acute activation of nSMase plays an important role in mechanical regulation of the heart, while chronically elevated membrane stretch results in constitutively increased nSMase activity which suppresses cardiac function, leads to structural remodeling and promotes cardiac arrhythmias. We further hypothesize that pathological nSMase signaling, including an increasing ceramide production and accumulation in damaged myocardium, may serve as an early ACM disease marker to detect preclinical ACM for genetically predisposed persons and identify people for whom early disease- modifying treatments may be appropriate. The research has high potential because the mechanistic insights gained from it can serve as a foundation for recognition of potential biomarkers of myocardial damage and identifying novel therapeutic targets aimed at prevention of cardiac remodeling during chronic overload.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY We can learn and successfully recall faces, events, language, concepts, places, facts, things that were frightening or rewarding, and the movement commands required to skillfully move our motor effectors. Decades of scientific research have pointed to the role of synaptic plasticity as the basic currency of the brain’s ability to learn and remember. While we have a fairly detailed understanding of the rules that govern changes in synaptic strength and modifications of a neuron’s intrinsic membrane excitability, our understanding of how these plastic changes lead to behavioral learning and memory is still in its infancy. Behavioral learning is an emergent property of a complete neural learning circuit in which the sites and mechanisms of plasticity are embedded. Without an understanding of the effects of plasticity at the circuit-level, we cannot truly understand learning and memory. Arguably, motor adaptation is the domain where we have best chance to understand the circuit-level rules that govern learning, due to the exquisite relationship between sensory stimuli and adaptive behavior. The cerebellum has been shown to be the brain structure crucial for motor learning, and provides a neural locus to begin to outline the circuit rules that govern learning. Our goal is to leverage the highly conserved cytoarchitecture of the cerebellar circuit to identify the principles of operation that underpin neural learning circuits more generally. During the mentored phase of this award, we will focus on a well-described cerebellar-dependent behavior: pursuit direction learning. Even after the occurrence of a single movement error in this task, the brain learns from the mistake, attempting to minimize the error in the next trial. In the first aim, we will characterize the signals that drive the error-dependent acquisition of this motor memory at a specific synapse in the cerebellar circuit, which is thought to contribute to the bulk of behavioral motor learning. During the second aim, we will record from the complete cerebellar circuit. Our goal is to describe how individual elements and synapses in the cerebellar circuit contribute to behavioral adaptation, including constraints on the site(s) of plasticity that cause behavioral learning and allowing conclusions about the extent to which learning occurs before, inside, or downstream of the cerebellar cortex. During the independent phase, we will again record from the complete cerebellar circuit during a different cerebellar learning task: saccadic adaptation. Using an adaptive behavior that relies on a different cerebellar region, we can begin to dissect the circuit-level principles that generalize broadly across cerebellar learning. Together, our results will provide the first circuit-level rules that underlie behavioral learning. These results should have broad implications across other learning and memory systems, all of which exist as complex circuits that drive behavior.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The translation gap between evidence and practice that leaves many evidence-based innovations (EBIs) underutilized is well recognized, prompting the development of implementation science. While the field has made notable progress in implementing practices with targeted individuals and organizations, it has given less attention to and made slower progress across broader provider populations, limiting its overall impact on the translation gap. This three-arm comparative effectiveness cluster randomized controlled trial will evaluate a systems change approach to scaling medications for opioid use disorder (MOUD) across 120 prisons in 12 states. Policy levers and multisite learning collaboratives, two promising, cost-effective, scale-up implementation approaches used in health care, education, and community development in developing countries, will be tested for their effectiveness in implementing MOUD in prison settings. This focus is driven by strong evidence supporting MOUD efficacy, poor MOUD penetration rates in prisons, existing health inequities within incarcerated populations, and the high risk of preventable overdose deaths for those transitioning from criminal legal settings to the community. Prisons were selected as the criminal justice setting (CJS) for this trial because they house 58.5% of the incarcerated population. The policy portion of the trial will capitalize on a natural experiment that exists in the Substance Abuse and Mental Health Services Administration (SAMHSA) state-based Policy Academy, which provides technical assistance to state Departments of Corrections (DOCs) on use of policy lever bundles to increase prison-based MOUD use. The learning collaborative will use a standardized approach, starting with didactic education sessions followed by monthly organizational coaching sessions for 12 months, focusing on prison level rather than DOC-level scale-up of MOUD. A RE-AIM evaluation approach will be applied, with the primary aim based on reach, measured through standardized MOUD unit purchases that can provide an innovative way to measure MOUD use in each prison, clustered by state. The trial will compare the differential reach of a policy lever implemented through the Policy Academy with that of the learning collaborative, as well as the impact of contextual variables on both these strategies compared to practice as usual. In this complex environment where many factors can impact the uptake of an EBI, a sophisticated covariate strategy will be used to isolate scale-up treatment effects, and a qualitative and economic analysis will be used to interpret those effects. Overall, it is estimated that increased use of MOUD in prison settings can reduce mortality and morbidity, lower healthcare costs, and improve quality of life. Scale-up and capacity-building activities to improve the delivery of EBIs may be used at other key points in the Sequential Intercept Model (SIM), which details how individuals with mental and substance use disorders come in contact with and move through the CJS. Both interventions being tested are replicable and, if successful, could significantly advance the translation and scale-up of EBIs within CJS and beyond.

NIH Research Projects · FY 2026 · 2025-09

PROJECT SUMMARY Cognitive impairments in mathematics affect 3-6% of children in the United States. These impairments often involve one of the earliest emerging and foundational number abilities, the Approximate Number System. The Approximate Number System allows young children to tell more from fewer items and is thought to be crucial to the development of early mathematics abilities, such as learning the meaning of number words. However, we do not currently know the factors that contribute to the development of the Approximate Number System. The proposed research will increase our understanding of typical development of this system and has the potential to inform best early educational practices to help this system develop, aid in the early diagnosis of mathematic learning disabilities affecting this system, and provide an empirical basis for the development of targeted interventions. Here, we propose to measure the role of neural maturation, experience, and genetics on the development of the Approximate Number System through a combination of child development methods, comparative developmental methods with infant primates, and computational methods to evaluate a wide range of alternative hypotheses. We will use simple number estimation tasks and computerized training tasks that can be implemented in a wide range of ages and cross-species. This allows us to compare the developmental progression across two different models and test for differences due to neural maturation rate (monkeys have much more rapid neural development compared to humans). Using developmental methods in primates, we can isolate and test individual factors that contribute to this system in ways that have not been possible before, including testing the role of genetic influences (using detailed pedigree information from the primate colony) and measuring the role of experience in tightly controlled rearing environments. The proposed research thus stands to break substantial new ground in the methods that are used to study child development and has unique ways to test the assumptions from many influential developmental theories about the relative role of genetics, maturation, and experience in cognitive development. Insights about the role of these factors in early number development will have implications for our understanding of learning and development more broadly.

NIH Research Projects · FY 2025 · 2025-09

Abstract Bacterial bile acid (BA) transformations have been studied for decades due to their relevance in several hu- man metabolic disorders and cancers. While the genes responsible for BA transformations have been the fo- cus area for many studies, their identification in bacterial genomes has proved a poor predictor of activity. Without a fundamental understanding of the factors that regulate BA transforming activity, the development of therapeutic interventions that leverage bacterial BA transforming activity to diagnose and treat human meta- bolic disorders remains elusive. The overall objective of this proposal is to determine how environmental fac- tors influence the regulation of genes responsible for BA transformations in communities of gut bacteria. The central hypothesis is that BA transforming activity is context dependent, impacted by the composition of the BA pool and host diet. This hypothesis was formulated based on preliminary data demonstrating that cocultured bacteria compete for available BA substrates, sometimes excluding expected BA transformations from occur- ring. In addition, bacterial BA transforming activity varies as a function of BA concentration and nutrient availa- bility. The rationale for this proposal is that bacterial BA transforming activity is regulated by physiologically rel- evant environmental factors that dictate the network of transformations that occur. The central hypothesis will be tested by two specific aims: 1) determine how BA composition shapes bacterial community composition and transforming activity under physiologically relevant conditions and 2) define the mechanistic contribution of die- tary fat on bacterial BA transforming activity in mice. For Aim 1, small synthetic communities of bacteria with varying capacity for BAs transformations will be tested for activity under a range of BA pools and BA concen- trations. Multi-omics analyses, in combination with quantitative BA measurements, will be performed to deter- mine how BA transforming pathways are regulated under these conditions and a multivariate multiple regres- sion analysis will be used to determine which factors are most likely to predict altered growth and BA trans- forming activity. For Aim 2, conventionally raised and gnotobiotic mice will be tested for their BA transforming activity on high-fat and low-fat diets. 16S sequencing will be used to determine bacterial community composi- tion and RT-qPCR will be used to measure host and bacterial gene expression related to BA transformations. The proposed research is significant because it will provide the fundamental knowledge needed to develop ef- fective therapeutic interventions capitalizing on the gut microbiome and BA pools to improve human health. The proposed aims will allow the applicant to acquire training in 1) the collection and analysis of multi-omics datasets and 2) the use of animal models to disentangle complex interactions between diet, the gut microbiota, and BA metabolism, which are essential to a career as an independent researcher in academia.